Progress report for SW21-921
Project Information
Evolving consumer interest in more localized and sustainable meat production, particularly in the time of COVID-19, is creating new market opportunities for livestock producers, meat processors and other meat supply chain participants. Studies throughout the United States have documented consumer willingness to pay for sustainability attributes and specialized production and handling methods, but the skills and resources needed for producing and processing livestock, and selling meat products profitably into local food markets (defined as direct and intermediated market channels) are difficult to access and adapt to values-based, consumer-focused business models.
Leveraging the Niche Meat Processor’s Assistance Network’s resources, as well as Cornell University’s NESARE-funded work in Massachusetts, this project will create financial benchmark and pricing resources that support advanced training curriculum and improve meat supply chain coordination in Wyoming, Colorado and Montana. We will do this by integrating restricted access farm financial data into educational programs and materials, thereby increasing producers' access to and ability to act on local food market channel opportunities that best meet the comparative advantage of their operation. In addition, we will convene stakeholders throughout the meat supply chain in an effort to improve coordination, reduce transaction costs, and thus improve producer profitability. Specifically, this portfolio of education and outreach activities will include: 1) two regional meat conferences (modeled after the highly successful Carolina Meat Conference); 2) online meat school classes to reduce risk and support producer profitability in new markets; and 3) augmenting Cornell’s online pricing tool with cost of production data to support improved decision making around markets channel selection.
This project includes two research objectives (RO) and three education objectives (EO): RO1) Evaluate how a large, multi-sectoral event such as a meat conference (incorporating at least 400 producers, meat processors, and meat supply chain stakeholders) results in measurable changes in connections and relationships throughout the meat supply chain, including across rural and urban stakeholders; RO2) Examine multiple years of USDA Census of Agriculture data and conduct empirical analysis to investigate how participation in local food market channels (i.e., farmers market, other direct, retail, distributor/institution), as well as operation / producer characteristics, and locational variables impact the profitability of livestock producers in the Western U.S. The Education Objectives include: EO1) Enhance at least 60% of producer attendees’ knowledge of and connections to meat supply chain partners within Colorado, Montana and Wyoming through a multi-state meat conference held in 2021 and 2023; EO2) Assist at least 90 livestock producers (30 in each state) in identifying and managing production, processing and marketing risks to their meat businesses through targeted classes on sustainable livestock production practices, strategies for improving meat processing, and identifying and building new markets for their meat products, with at least 60% improving the profitability of an existing or new marketing channel; and EO3) Encourage and instruct at least 30 livestock producers (10 in each of 3 states) to understand and estimate their costs of production, and develop retail and wholesale pricing models to assess and improve their profitability in different market channels.
Research Objectives:
- Evaluate how a large, multi-sectoral event such as a meat conference (incorporating at least 400 ranchers/producers, meat processors, and meat supply chain stakeholders) results in measurable changes in connections and relationships throughout the meat supply chain, including across rural and urban stakeholders.
- Examine multiple years of USDA Census of Agriculture data and conduct empirical analysis to investigate how participation in local food market channels (i.e., farmers market, other direct, retail, distributor/institution), as well as operation / producer characteristics, and locational variables impact the profitability of livestock producers/ranchers in the Western U.S.
Education Objectives:
- Enhance at least 60% of livestock producer/rancher attendees’ knowledge of and connections to meat supply chain partners within Colorado, Montana and Wyoming through a multi-state meat conference held in 2021 and 2023.
- Assist at least 90 livestock producers (30 in each state) in identifying and managing production, processing and marketing risks to their meat businesses through targeted classes on sustainable livestock production practices, strategies for improving meat processing, and identifying and building new markets for their meat products, with at least 60% improving the profitability of an existing or new marketing channel.
- Encourage and instruct at least 30 livestock producers (10 in each of 3 states) to understand and estimate their costs of production, and develop retail and wholesale pricing models to assess and improve their profitability in different market channels.
Cooperators
- (Researcher)
- - Technical Advisor - Producer
- - Technical Advisor - Producer
- - Technical Advisor - Producer
- - Technical Advisor - Producer
- - Technical Advisor - Producer
- - Technical Advisor
- - Technical Advisor
- (Researcher)
- - Technical Advisor - Producer
- - Technical Advisor (Educator)
Research
- Most producers do not know their costs of production. Providing information on costs of production that they can enter into the pricing tool and use to set pricing will lead to better decision-making and thus make their operations more profitable and sustainable.
- There are noticeable disconnects throughout the supply chain. Through intentionally designing regular convenings there are opportunities to build trust that will strengthen regional meat supply chains and result in improved sustainability outcomes for ranchers, processors, and regional communities and economies.
RO1. Evaluate how a large, multi-sectoral event such as a meat conference (incorporating at least 400 ranchers/producers, meat processors, and meat supply chain stakeholders) results in measurable changes in connections and relationships throughout the meat supply chain, including across rural and urban stakeholders.
As the number of opportunities for values-based procurement and claims-based markets grow, for livestock producers to effectively benefit from these opportunities there must be enhanced communication and understanding across supply chains - from production to consumers. This becomes more of a challenge as meat supply chains are highly consolidated (e.g., MacDonald et al. 2000), limiting direct communication between producers and consumers across the supply chain. Carolan (2020), for example, conducted interviews with individuals engaged in shaping urban food policy and institutional procurement plans in Denver, and rural Colorado farmers and ranchers. He found important differences across the two groups in terms of conceptions of “good food” and what it means to be a “good farmer”; reconciling these requires mediation.
Accordingly, we are proposing the rigorous evaluation of a meat conference, to be held twice during the grant period. The purpose of this research objective is to evaluate how an event that intentionally brings together stakeholders along the meat supply chain and incorporates activities designed to build bridges can result in measurable changes in connections and relationships, including across rural and urban stakeholders. To do this we will follow methods used by Brasier and Goetz (2010) and Love et al. (2020). Brasier and Goetz (2010) analyzed the number of new connections made at a local foods conference, particularly for those with few professional linkages prior to the conference. Love et al. (2020) analyzed how different educational curriculum in a construction management program impacted social networks.
Data: Data collection will center around the meat conferences held in year one quarter four in Colorado, and year three quarter four in Montana. For each meat conference, registrants will be surveyed before and after the event. We expect that at least 200 individuals will participate in each year of the meat conference, and participation in the pre survey will be required for all attendees to attend the event. To improve the response rate following the event, we will raffle an iPad. Accordingly, we estimate that at least 100 individuals will participate in both the pre and post survey, thus providing useable data for our social network analysis.
Participants will be asked to identify the types of relationships they have with different types of stakeholders across the meat supply chain before and after the event. For each question, they will add an individual name and organization name with whom they have had a written or verbal exchange about something pertaining to meat within the past year. Who they identify will be piped through to future sections of this survey where they will be asked to answer additional questions about these individuals/organizations.
Additionally, respondents will be asked questions regarding their view of different claims-based language and labels including “sustainable” “regenerative” and “grass-fed”. This will help us to discern if changes in values, production practices, or market opportunities were related to changing supply chain relationships. Finally, producers will be asked pre/post questions related to pre/post markets, prices, and profitability to enable us to understand if new relationships were formed that impacted sales.
Methods: Social network data will be analyzed statistically using UCInet (Borgatti and Freeman 2002), and social network diagrams will be created using Visone (Brandes and Wagner 2004). Density, a measure of the number of actual connections in a network divided by the number of possible connections (Giuffre 2013), will be used to measure ‘within’ network connections and to explain network cohesion. In this research, we will use density to compare changes from the pre/post surveys. Additionally, we will calculate fragmentation in the network, a whole network statistic that measures the proportion of pairs of nodes that cannot reach each other (Hanneman and Riddle 2005).
Three network statistics will be used to evaluate the network as well as changes within and across the network. First, average degree is a measure of the number of connections for each actor in the network (Giuffre 2013). Second (described previously), we will calculate the fragmentation in the network. Third, Freeman’s centralization will be used to characterize the overall structure of a network through “in” and “out” measures of centrality. When a network is more centralized, it indicates that the few central individuals are more influential than those on the periphery. In contrast, a network with low centrality shows that social relationships and influence are more evenly balanced across the network.
Baran (1964) described the difference between three ideal types of network structures: a) a centralized star-like network, b) a decentralized network, and c) a distributed un-centralized network. The key characteristic of a centralized network is its star-like pattern. Centralized star-like networks are vulnerable because the removal of a key central node can cause all other nodes to become disconnected from each other (this is what we might expect to see in the ‘pre’ meat supply chain, as it is generally only the processor or distributor that communicates with both the livestock producer and buyer). A decentralized network also has a star-like pattern, and is still considered a vulnerable or imbalanced network because the central nodes have more influence. In contrast, in a distributed network, all nodes can connect and communicate with other nodes, thus reducing the imbalance of power found in centralized networks. If the meat conference is successful in strengthening ties throughout the supply chain, this is the type of result we would expect to see.
RO2. Examine multiple years of USDA Census of Agriculture data and conduct empirical analysis to investigate how participation in local food market channels (i.e., farmers market, other direct, retail, distributor/institution), as well as operation / producer characteristics, and locational variables impact the profitability of livestock producers/ranchers in the Western U.S.
Recent national research on the profitability impacts of sales through local food markets (e.g., Bauman et al. 2018a, 2018b; Jablonski et al. 2020) provides important preliminary information for producers making determinations about market channel selection. Recent research by Jablonski and Bauman, for example, use USDA ARMS data to create financial benchmarks for farms and ranches with sales through local food markets, provide preliminary estimates of profitability implications of local food marketing strategies, investigate the role of human capital measured in terms of wage rates and labor expenditures, and evaluate the financial efficiency of local food producers (fact sheets based off of peer-reviewed research can be found here: https://localfoodeconomics.com/benchmarks/#factsheetsection). As an example of some of the analysis used in this research, Bauman et al. (2018) divide the sample by gross cash farm income, as well as quartile within each sales class (i.e., quartile four is the most profitable and quartile one the least profitable) to investigate the relationship between key financial characteristics and profitability for farms selling through local markets. Across sales class ($1,000-$74,000, $75,000-$349,999, $350,000-$1M, and >$1M), each of the quartile groups are significantly different at the one percent level. They find that among the top performing quartiles, direct-to-consumer only marketers had lower return on assets (their measure of profitability) compared to top performers using intermediated only markets or a combination of direct-to-consumer and intermediated markets. Bauman et al. (2019) explored the financial efficiency of farms and ranches that sell through local food markets to better understand what factors could improve their viability and performance. Profit efficiency frontier maps the combinations of fixed and variable inputs that are utilized by the most profitable local food producers. They find that, on average, a farm or ranch could increase profit by about 133% by improving efficiency (i.e,. utilizing a different combination of fixed and variable inputs). Overall, most farms and ranches using local food markets are not producing on the efficiency frontier and could realize significant improvements in profitability with changes in their operation.
Though this preliminary analysis is important - and points to the importance of understanding the relationship between market channel selection and production characteristics - more in-depth analysis is needed to provide information to producers that can meaningfully inform market channel selection. This research objective will utilize the USDA ARMS data, focused on livestock producers in the Western US, to examine the relationship between market channel selection and production, demographic and locational characteristics.
Data: USDA National Agricultural Statistics Service (NASS) administers a Census of Agriculture every 5 years to collect data on all U.S. farms and ranches where $1,000 or more of agricultural products were (or could have been) produced or sold. The survey is mandatory and produces county-level agricultural activity and productivity totals, with a focus on measuring farm business performance. It provides the most accurate data to track operations over time and includes data on demographics, commodities, sales and expenditures, and farm-level profitability. It has the largest sample size of local food producers and is the only dataset that allows for county-level analysis. It can be used to evaluate the scale and scope of the local and nonlocal food sectors as well as farm financial impacts. We use the restricted access, farm-level data to track individual livestock operations over time.
PI Jablonski and Bauman have utilized these data to analyze profitability impacts of farms and ranches selling through local food markets for two forthcoming USDA Economic Research Service Reports.
Methods: Using data from the 2017 and 2022 Census of Agriculture, we compared the characteristics of livestock operations with local food sales that remained in business to those that did not have local food sales. The study focused on understanding differences across local and nonlocal livestock operations.
We are in the process of integrating this information into two U.S. Department of Agriculture Economic Research Service publications. The first is an update to the 2015 Trends in Local and Regional Food Systems report. The second is a report on beginning farmers. We were granted access to the restricted access 2022 Census of Agriculture data later than anticipated, but we expect to have results that we can integrate in the next two months. In the meantime, we present relevant results from prior time periods. Due to structural changes since 2020, is it possible that some of the relationships found historically will have changed.
Using data from the 2013 ARMS, we grouped operations by primary commodity and return on asset (ROA) quartiles. We found a wide range of profitability (defined as ROA) for livestock/dairy operations selling through local food marketing channels, with those at the top end being very profitable. The ROA for livestock/dairy operations selling through local markets ranged from very low in quartile 1, average ROA -151, to high in (quartile 4), average ROA of 23. When comparing the ROA of livestock/dairy producers to that of fruit/vegetable and field crop producers, we saw a similar pattern across the first three quartiles, and statistically significant differences at the top end (quartile 4). Livestock/dairy producers in the 4th quartile had an ROA of 23 and compared to 33 for fruit/vegetable producers and 13 for field crop producers.
In another study using 2013 and 2014 ARMS, we found that local food operations selling primarily fruits and vegetables are more profitable than those selling primarily field crops or livestock/dairy. Results indicate that, all else equal, livestock/dairy producers with local food sales are less efficient than fruit/vegetable producers with local food sales.
Additionally, we have presented these results at many extension and government oriented meetings, including:
- 2024, Beginning Farmer and Rancher Operations: Characteristics Associated with Business Survival, U.S. Department of Agriculture Department-wide Beginning Farmers and Ranchers Working Group, virtual
- 2023, Considerations for successful beginning farmer and rancher development programs, invited presentation to the U.S. Department of Agriculture National Institute of Food and Agriculture Beginning Farmer and Rancher Development Program Project Directors, Denver, CO
- 2022, U.S. Beginning farmers and ranchers, Invited presentation to the U.S. Department of Agriculture Beginning Farmer Program, virtual
- 2022, Local and regional food systems, National Farm Business Management Conference, Fort Collins, CO
- 2022, Understanding and Supporting a Continuum of Agricultural Businesses: Succession Relationships for YBS Farmers and Ranchers, The National Forum on Serving Young, Beginning, and Small Farmers and Ranchers, Farm Credit Administration, Fort Collins, CO
- 2022, Data + Metrics: Helping Markets Make the Case through Research and Dashboards, InTENTS, 2022 Annual Meeting of the National Farmers Market Association, San Diego, CA
Additionally, we’ve been able to leverage these funds to get additional external grants, including:
- (2022-2024) Helping beginning growers analyze market channels to improve profits, Beginning Farmer and Rancher Development Program, U.S. Department of Agriculture, National Institute of Food and Agriculture, $744,103 (CSU subcontract $74,918, University of Minnesota lead).
- (2023-2027) SARE Meat and Poultry Processing Project for the West, $419,900 (MSU contract, for work in the Western region).
Research Outcomes
Meat Summit Research-Related Outcomes (text)
Our research objective with the 2023 Mountain Meat Summit, held in Fort Collins, CO was to evaluate how this event could result in measurable changes in connections and relationships throughout the meat supply chain, including across rural and urban stakeholders. Building networks and connections took place over two days through:
- Tours of three different scales of livestock and meat enterprises;
- Exposure for Mountain Meat Summit participants to industry sector updates from CattleFax, the US Meat Export Federation, and CSU AgNext;
- Panel discussions with multiple stakeholders, across geographies (for example, two states’ departments of Agriculture and meat producers from the Western region);
- Curbside consulting opportunities representing nine different technical assistance providers who could help Summit participants identify new resources to build new plants or markets or improve existing resource allocation for their operations; and
- Multiple networking sessions throughout the second day’s program at the Colorado State University campus in Fort Collins.
Of 173 attendees at the two-day event, we obtained 93 usable responses for the pre-survey and 50 usable responses for the post-survey. Linking pre and post survey respondents, we obtained 38 respondents for whom we could measure specific outcomes from participating in the conference. For example, 8 ranchers/producers responded to the pre and post-surveys indicating they made 102 new meat supply chain connections, primarily with other ranchers. Interestingly, educators and researchers made the greatest number of new meat supply chain connections, followed by ranchers and then by input suppliers and service providers. Table 1 below summarizes some of these results.
Table 1. Number of new meat supply chain connections by role in the livestock/meat industry, 2023 Mountain Meat Summit.
|
|
|
Ranchers |
Processors |
Retailers |
Restaurants |
Input suppliers, service providers |
Educators, researchers |
|
Ranchers (n=8) |
Total |
30 |
11 |
10 |
21 |
9 |
21 |
|
Average |
3.8 |
2.8 |
3.3 |
5.3 |
3.0 |
3.5 |
|
|
Processor (n=5) |
Total |
16 |
10 |
7 |
0 |
4 |
15 |
|
Average |
5.3 |
5.0 |
7.0 |
- |
2.0 |
3.0 |
|
|
Retailers (n=3) |
Total |
7 |
5 |
5 |
3 |
3 |
12 |
|
Average |
3.5 |
2.5 |
5.0 |
3.0 |
3.0 |
4.0 |
|
|
Input suppliers/ service providers (n=8) |
Total |
26 |
28 |
6 |
0 |
1 |
27 |
|
Average |
5.2 |
4.7 |
3.0 |
- |
1.0 |
3.9 |
|
|
Educator/ researcher (n=14) |
Total |
30 |
21 |
10 |
3 |
13 |
36 |
|
Average |
3.5 |
3.7 |
1.7 |
3.0 |
2.2 |
3.7 |
We also looked the level engagement that certain supply chain stakeholders had before the event, based on how often they connected with other supply chain actors at intervals of: 1) more than one time per month; 2) 5-10 times per year; 3) less than 5 times per year and 4) no interaction at all.
For example, ranchers with different levels of engagement in the supply chain before the Summit made significant connections with new stakeholders, especially among those who came to the event with a high level of connectivity (more than one time per month). Those individuals generally made between 3 and 5 new connections, on average, with other ranchers, processors, buyers, restaurants and educators/researchers. Those with moderate connectivity before the event made fewer new connections overall, but those with low connectivity engaged with other ranchers, new processors, service providers and educators/researchers. Lastly, those participants with no pre-event connections met some new supply chain actors with whom they intend to connect in the coming months.
We saw similar patterns across processors and buyers who attended the Summit. Processors in the high engagement category gained a variety of new connections at the conference, including with ranchers, other processors, restaurants, and educators/researchers. Those with low pre-event connectivity (that is interactions of fewer than 5 times per year) made significant new connections to build on in the coming months. Finally, buyers (retail and wholesale) made significant new connections across all stakeholder groups except input and service providers (and these may be less relevant to their role in the supply chain). Even those who reported low to no pre-event interaction with supply chain actors connected with ranchers and processors. In sum, the Mountain Meat Summit provided multiple ways for supply chain participants to forge new connections, whether they attended the event with a broad set of connections or they used the Summit to grow their engagement in the sector.
We should note that we had originally planned on conducting a complete social network analysis to be able to map out the direction and strength of existing and new connections resulting from the Summit. However, this approach was limited by a sponsor request to refrain from contacting certain participants (primarily students and those who typically attended the International Livestock Forum (our complementary event). This necessarily limited our pre- and post-Summit survey to a much smaller group of participants.
In the post survey we asked participants to list three new business activities they planned to engage in over the next six months as a result of attending the ILF/MMS event. More than one-fifth mentioned activities related to improving or maintaining the networking that they began at the ILF/MMS event, while others indicated they wanted to offer education based on information they gained. The table below summarizes these responses, indicating a variety of actionable business activities that participants would pursue.
Table 2.Planned new business activities from 2023 Mountain Meat Summit
|
New business activity |
Percent of total responses |
|
Improve/maintain networking |
22% |
|
Offer new education based on ideas gained |
19% |
|
Expand/explore new markets |
17% |
|
Explore grants/funding opportunities |
11% |
|
Improve own business practices |
11% |
|
Look at new/improved processing options |
7% |
|
Use industry publications/info to stay up to date |
6% |
|
Use Cornell Meat Price Calculator |
4% |
|
Address workforce issues |
2% |
|
Address biosecurity |
2% |
We held a second Mountain Meat Summit in May 2024 in Bozeman MT, also seeking to to evaluate how this event could result in measurable changes in connections and relationships throughout the meat supply chain, including across rural and urban stakeholders. This event brought together over 130 key figures in the Mountain West's local and regional meat supply chain, including chefs, processors, educators, and producers. Based on outcomes from the 2023 Mountain Meat Summit, the 2024 event was dedicated to advancing market opportunities, building connections, and enhancing skill development across the region, with a strong emphasis on business-to-business collaboration and insightful presentations from industry leaders and academics.
The Summit combined educational tours, technical sessions, and numerous networking opportunities where meaningful conversations and practical connections were made-often over locally sourced meat dishes. The event concluded with a consulting and education fair, featuring samples and presentations by local charcuterie makers.
For this event, one survey was administered that combined both a pre and post survey instrument. Of 130 participants, we obtained 57 responses from which we could measure outcomes across conference participants. Of those who completed the pre and post surveys, 20% were ranchers; 16% were processors; 7% were buyers; 43% were input suppliers or service providers; and 14% were educators and/or researchers.
The chart below shows that each participant group had different areas of key learning, including some groups indicating that they gained no new knowledge in several areas (i.e., processors reported n new knowledge gained in understanding the regulatory environment, product differentiation and processing. Ranchers reported knowledge gained in all areas, as did input suppliers/service providers, and educators/researchers.
In terms of understanding and building networks, Summit participants told us how frequently they interacted with other meat supply chain professionals both before and after the event. In general, ranchers reported the most frequent contact with other ranchers, with processors, with input suppliers/service providers and educators/researchers. Ranchers did not report that this activity would increase significantly; however, they did feel they would have more interaction with restaurant buyers and chefs following the event. Processors indicated that they would have more interaction with educators/researchers, and buyers indicated a slight increase in connectivity with processors, chefs, suppliers/service providers and educators/researchers. The greatest gains in professional interactions appeared among input suppliers/service providers and educators/researchers who expected to interact with all meat supply chain participants on a more regular basis.
Looking ahead to how the Summit might influence future networking, we asked participants to estimate the number of direct connections they thought they would contact in the 6 months following the Mountain Meat Summit. Similar to our findings from the 2023 Summit, ranchers estimated that they would make the most connections of all supply change stakeholders at 307 total, followed by processors (231) and educators/researchers (210). The table below provides details on how each group of participants anticipated connecting with others, in terms of the total number and then the average per business relationship.
Table 3. Supply chain connections resulting from 2024 Mountain Meat Summit
|
|
Ranchers |
Processors |
Buyers |
Chefs |
Input suppliers |
Educators |
|
|
Ranchers (n=9) |
Total |
26 |
13 |
15 |
15 |
9 |
13 |
|
Average |
5.20 |
2.17 |
3.00 |
3.75 |
3.00 |
2.60 |
|
|
Processors (n=7) |
Total |
62 |
28 |
63 |
44 |
14 |
15 |
|
Average |
12.40 |
7.00 |
12.60 |
11.00 |
4.67 |
3.75 |
|
|
Retailers (n=3) |
Total |
20 |
20 |
14 |
15 |
11 |
10 |
|
Average |
20.00 |
20.00 |
14.00 |
15.00 |
11.00 |
10.00 |
|
|
Input suppliers (n=19) |
Total |
124 |
118 |
58 |
51 |
60 |
76 |
|
Average |
8.27 |
7.38 |
4.46 |
4.64 |
5.45 |
5.43 |
|
|
Educators (n=19) |
Total |
75 |
52 |
29 |
14 |
36 |
96 |
|
Average |
5.77 |
4.33 |
4.14 |
2.33 |
4.00 |
6.86 |
|
|
|
|||||||
|
Total |
307 |
231 |
179 |
139 |
130 |
210 |
|
|
Total |
Average |
7.87 |
5.92 |
5.77 |
5.35 |
4.81 |
5.53 |
|
|
When asked how the conference will impact the way participants do business in the future, attendees responded as follows in Table 4.
Table 4. Business impact of 2024 Mountain Meat Summit
|
Business impact |
Percent of attendees |
|
Networking that will advance business objectives |
43% |
|
Provided foundational education on industry trends and supply chains |
30% |
|
Information to improve workforce training |
10% |
|
Unsure of impact |
7% |
|
Pricing update and info on alternative products |
3% |
|
Production will increase |
3% |
|
Not applicable |
3% |
Additional results pertaining to the 2024 Mountain Meat Summit are attached here: 2024 Mountain Meat Summit evaluation summary.
Calculator Research-Related Outcomes (text)
We have spent the last year testing and refining the cost of production calculator, working with Cornell University to build in additional functions based on feedback from producers throughout the region. Below, we provide a detailed description about the data we use and choices that we made to better understand specific types of ranchers and to provide information so that they can make more informed pricing and marketing decisions.
Data
We used 2017 Census of Agriculture microdata to estimate the average cost of production for livestock operations selling through local food market channels (including direct-to-consumer and intermediated markets). In the Census of Agriculture, a farm is defined as a place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year. This very liberal definition of a farm captures a wide range of farms, including those that are not aspiring to be commercially viable operations. To capture only commercial operations, we drop all observations with less than $1,000 in sales.
Our sample consists only of non-diversified operations, i.e., operations that sell one species and no crops, so we can attribute all costs of production to only the species of interest. Farms are allowed to grow crops but have zero sales, implying that those costs accrue to feed requirements for the livestock enterprise. To capture cost of production differences across scale, we separate our sample, based on the number of head sold in a year, into small operations (<25th quantile), mid-size operations (25th to 75th quantile) and large operations (76th to 95th quantile). We drop observations above the 95th percentile as outliers. Species groupings include (1) non-dairy cattle, (2) hogs and pigs, and (3) sheep, lamb, and meat goats. For non-dairy cattle operations, we drop operations selling zero cattle weighing 500 pounds or more. We provide the expenses as a cost per head by species, scale, market channel, and region.
Methods
Given the large differences in costs across scale, we separate our sample by scale, based on head sold. For each species, we have three categories: small operations (<25th quantile), mid-size operations (25th to 75th quantile) and the large operations (76th to 95th quantile). We drop observations above the 95th percentile due to outliers.
- Cattle: Cattle sold or moved from this operation in 2017, including calves weighing 500 pounds or more, local only
o Small scale (<25th quantile): <2 head
o Mid-scale (25th to 75th quantile): 2 to 8 head
o Large scale (76th to 95th quantile): 9 to 30 head
- Hogs and pigs: Number of hogs and pigs sold or moved from this operation during 2017,
including feeder pigs, local only
o Small scale (<25th quantile): <6 head
o Mid-scale (25th to 75th quantile): 6 to 27 head
o Large scale (76th to 95th quantile): 28 to 200 head
- Sheep, lamb and meat goats: Number of sheep, lamb, and meat goats sold or moved from this operation during 2017, local only
o Small scale (<25th quantile): <10 head
o Mid-scale (25th to 75th quantile): 10 to 23 head
o Large scale (76th to 95th quantile): 24 to 62 head
Our sample consists of only those producers selling exclusively through local food market channels (i.e., they do not sell through both local and commodity market channels). Local food market channels include both direct-to-consumer market channels (i.e., farmers market, on-farm stores or farm stands, roadside stands or stores, u-pick, CSA, and online marketplaces) and intermediated market channels (i.e., supermarkets, supercenters, restaurants, caterers, independently owned grocery stores, food cooperatives, K-12 schools, colleges or universities, hospitals, workplace cafeterias, prisons, and food banks). Due to sample size issues, we do not disaggregate direct-to-consumer and intermediated within our local food market channel category.
All estimates are broken out by region as well as provided nationally. Regions are based on the census regions but with the west disaggregated into census divisions. Regions include Pacific (WA, OR, CA), Mountain (ID, NV, MT, WY, UT, AZ, CO, NM), Midwest (ND, SD, NE, KS, MN, IA, MO, WI, IL, MI, IN, OH), South (OK, TX, AR, LA, KY, TN, MS, AL, WV, VA, NC, SC, GA, FL, MD, DE), Northeast (NY, PA, NJ, VT, NH, MA, CT, RI, ME). We also include data for cattle producers in Colorado as the sample size allows for a state-level estimate (this was the only state in the Mountain region for which this held true).
Production expenses
As an example, Table 5 provides production expenses per head for livestock operations in the Mountain region (Arizona, Colorado, Idaho, Montana, New Mexico, Nevada, Wyoming) with sales exclusively through local food marketing channels. Results are reported separately for mid- and large-scale operations. On average, we see lower expense per head for large-scale operations compared to mid-scale operations across all categories except for cash rent for land and buildings, rent, lease expense for machinery, property taxes.
Table 5. Expense per head for livestock operations in the Mountain region by scale
|
Expense per head |
2 to 8 head sold |
9 to 30 head sold |
|
Fertilizer, chemicals, seeds, and plants |
73.91 |
64.73 |
|
Breeding stock, other livestock purchased or leased |
680.94 |
446.43 |
|
Feed |
794.27 |
548.33 |
|
Gas, fuel, oil |
338.21 |
167.98 |
|
Utilities |
176.55 |
123.99 |
|
Repairs and maintenance |
481.94 |
213.67 |
|
Hired labor, contract labor, custom work, and machine hir |
293.1 |
(D) |
|
Cash rent for land and buildings, rent, lease expense for machinery, property taxes |
65.41 |
101.41 |
|
Rent, lease expense for machinery, equipment and farm share of vehicles, and property taxes |
690.15 |
264.94 |
|
Interest paid on real estate debt, interest paid on non-real estate debt |
442.55 |
204.91 |
|
Other production expenses, medical expense (medical supplies, veterinary, and custom services for livestock) |
359.58 |
168.78 |
|
Total production expense |
4331.19 |
2421.12 |
Statistics: Mean (se)
Source: U.S. Department of Agriculture, National Agricultural Statistics Service, 2017 Census of Agriculture, calculated by the author using the restricted access data.
Table 6 provides all production expense variables asked in the 2017 Census of Agriculture and the expense categories used in this analysis. Some expense categories were grouped together for those categories for which producers were less likely to have recorded their expenses disaggregated.
Each expense is provided on a per head basis. We report both the total production expense, total expense per head, and the expense in each category per head, including benchmark ranges.
Table 6. Description of production expenses
|
Variable (US Census of Agriculture) |
Production expense description |
|
k1501 + k1502 + k1503 |
Fertilizer, chemicals, seeds, and plants |
|
k1504 + k1505 |
Breeding stock, other livestock purchased or leased |
|
k1506 |
Feed |
|
k1507 |
Gas, fuel, oil |
|
k1508 |
Utilities |
|
k1509 |
Repairs and maintenance |
|
k1510 + k1511+ k1512 |
Hired labor, contract labor, custom work, and machine hire |
|
k1513 + k1514 + k1517[3] |
Cash rent for land and buildings, rent, lease expense for machinery, property taxes |
|
k1513 |
Cash rent for land and buildings – including grazing fees |
|
k1514 + k1517 |
Rent, lease expense for machinery, equipment and farm share of vehicles, and property taxes |
|
k1515 + k1516 |
Interest paid on real estate debt, interest paid on non-real estate debt |
|
k1518 + k1935 |
Other production expenses, medical expense (medical supplies, veterinary, and custom services for livestock) |
|
Sum of all expenses above |
Total production expense |
Detailed description of how commodity variables were calculated follow below:
Cattle
Cattle operations = if gross value of sales from cattle and calves (including sales under production contract) is equal to total value of production, then 1 otherwise 0
Hogs and pigs
Hog and pig operations = if gross value of sales from hogs and pigs (including sales under production contract) is equal to total value of production, then 1 otherwise 0
Sheep, lamb and meat goats
Sheep, lamb and goat operations = if gross value of sales from sheep, lamb, and meat goats (not including sales under production contract) is equal to total value of production, then 1 otherwise 0
Education and Outreach
Participation Summary:
These are described in the previous research section.
Meat Summit Education-Related Outcomes
Overall, the 2023 Mountain Meat Summit helped participants gain knowledge about the business constraints they had identified prior to attending. For example, processors indicated that they had 3 barriers addressed that they had identified, while ranchers and educators/researchers gained information on more than two of their constraints. Retailers and other buyers, in addition to input suppliers and service providers, had fewer than 2 barriers to business expansion addressed by the event (see table 7 below).
Table 7. Barriers to business expansion identified by participants, 2023 Mountain Meat Summit
|
|
Ave number of barriers addressed by conference |
Understanding customer tastes & preferences |
Information on product differentiation |
Processing options |
Regulations around meat sales |
Food safety |
Pricing for profitability |
Labor |
Improving technology |
Accessing capital |
|
Ranchers |
2.4 |
11% |
14% |
20% |
33% |
0% |
36% |
0% |
20% |
22% |
|
Processors |
3.0 |
21% |
14% |
13% |
17% |
0% |
14% |
0% |
20% |
22% |
|
Retailers |
1.7 |
0% |
14% |
13% |
0% |
0% |
14% |
0% |
0% |
0% |
|
Input suppliers, service providers |
2.0 |
32% |
29% |
13% |
17% |
50% |
0% |
0% |
30% |
22% |
|
Educators/ researchers |
2.5 |
37% |
29% |
40% |
33% |
50% |
36% |
100% |
30% |
33% |
Notes: 1) Although we asked about restaurant buyers and chefs, none attended the event; 2) no one who identified transportation as a barrier also acknowledged having gained any information about it, therefore these two elements were eliminated from the table above.
Primary Mountain Meat Summit education goals included increasing participants' knowledge on a range of meat-industry topics and enhancing connectivity across meat supply chain stakeholders. Knowledge gained varied across topics and stakeholders.
For our 2023 Mountain Meat Summit participants, we measured knowledge gains against the barriers they stated encountering to indicate how impactful our educational sessions were helping them with these key issues. In the post-survey, 2023 participants were asked to indicate if they learned something new regarding the barriers listed in the pre-survey (Figure 3). Seventy percent of the ranchers learned something new regarding developing pricing strategies that enhance their business profitability, and 57% said they had learned new information about the regulatory environment for meat sales.
The table below shows overall knowledge gains across all 2024 MMS participants, with pricing, labor and product differentiation being the areas where participants gained the most information.
Table 8. Knowledge gained by participants, 2024 Mountain Meat Summit
|
Subject area |
Percent knowledge gained |
|
Pricing and business profitability |
24% |
|
Labor recruitment and training |
20% |
|
Certifications or other ways to differentiate products |
18% |
|
Regulations around meat sales |
14% |
|
Processing options for meat products |
13% |
|
Food safety training/implementation |
11% |
|
Customer demand for meat products |
9% |
Calculator Education-Related Outcomes
Based on the above-mentioned activities, we now understand the cost of production associated with different species, scale, and market channel. Over the next year, we will work to finalize a draft of the online calculator.
We are now working to test the calculator and costs of production with stakeholders. First, we had five “experts” test the cost of production tool and calculator. These experts are part of the Colorado State University Extension team, AgNext, and the Colorado Beef Council. Based on their suggestions, we made changes to the cost of production estimator. View the survey here: Meat_pricing_calculator_survey
Subsequently, we created a list of livestock producers using local foods markets using information provided by the Colorado Department of Education (list of ranchers that sold to schools in 2022/2023), the CO Department of Agriculture (including their Farm Fresh Data and CO Proud directory), Mountain Meat Summit participants, and Meat School participants. Our list includes 210 producers.
Next, we developed a survey instrument. The inclusion/exclusion criteria is that the operator must be at least 18 years old or older, currently raising beef, sheep/lamp, goat, or pork, and sold through an intermediated market in 2023. The intent of the survey is to understand information about the operator and operation in advance of them trying out the calculator. Survey questions ask about farm characteristics (farm location, sales, number of livestock, other ag products produced), farm financials (if they know their variable expenses), market channels (what markets they use, experience in those markets, pricing strategies), and operator characteristics (demographics, beginning farmer status).
Once they complete the survey, they are asked to fill out the cost of production estimator and apply it to the price and yield calculator. When they complete this step, we provide them with a $100 gift card (funded through CSU and not the grant).
We just started to solicit producers to fill out the survey given that we wanted to wait until the winter. An undergraduate research assistant recently sent out a recruitment email. We intend to compile results in early 2025 (our goal is to have 50 responses) and then write up the results for publication in Agricultural Finance Review.
As broader outcomes from this project we would like to signal 3 opportunities that have resulted directly from our work that allow for expanding technical expertise and funding in our region:
1. The WSARE award SW21-921 helped build outreach capacity and further develop professional networks across the Northern Rockies and the West in general. For example, work completed, and relationships built helped bring $400K in National SARE funds earmarked for meat and poultry processing workforce development and local/regional supply chain coordination to the Western region. These funds, managed by Montana State University (Boles and Bass), have been dispersed for programming, educational materials production and translation to Spanish, and workforce scholarships in MT, WY, OR, ID, CO, NM, and AZ. While SW21-921 funds helped found the Mountain Meat Summits, the additional SARE funds also had a 15% investment in the 2024 Mountain Meat Summit (report).
2. Relationships and collegial infrastructure built through SW21-921 collaboration were the foundation for a strong meat processing component within the multi-million-dollar funded USDA AMS NW and Rocky Mountain Regional Food Business Center, serving CO, WY, MT, ID, OR, and WA. All three projects (WSARE, SARE, and NWRM-RFBC) continue to work synergistically to support stability and sustainable growth in local/regional meat supply chains. In particular, the Northwest and Rocky Mountain Regional Food Business Center is infusing new grant funding (on a competitive and non-competitive basis through our Business Builder program) and technical assistance through a focus on improving meat supply chains (https://nwrockymountainregionalfoodbusiness.com/animal/).
3. Lastly, we built part of the educational foundation for this WSARE project around previous work from colleagues at North Carolina State University who originated the Carolina Meat Conference. After holding the first Mountain Meat Summit, a group of collaborators from throughout the Intermountain West and other areas of the U.S. decided to form a National Meat Summit Steering Committee (NMSSC) which is now formalized as a working group and is raising funding to hold similar events throughout the U.S. which focus on connecting local and regional meat supply chains (https://meatsummits.com/). To that end, the NMSSC has supported the 2024 Carolina Meat Conference, and will support both the Midwest Meat Summit in January 2025 and the Black Belt Meat Summit in April 2025. The synergy resulting from this group and its activities is reinvigorating smaller-scale more localized educational and networking events across the U.S.
Education and Outreach Outcomes
Local and regional food markets present opportunities to support the sustainability of livestock systems in the Western U.S. Further, many of those markets (particularly intermediated markets) are growing. Accordingly, working with operations to better understand their costs of production when making pricing determinations can play important roles in the profitability of the operation. Additionally, there is evidence that creating opportunities for livestock operations to network with businesses across the supply chain is valuable in increasing market opportunities.
Our research on our two Mountain Meat Summit educational events demonstrated that participants found networking opportunities both at the event itself, as well as afterward once those connections had been identified. This translates into building thoughtful events in the future that allow time and structure for networking along the supply chain and even across business scales. Our project helped meat supply chain participants understand and contact the resources available to them (through educational sessions, curbside consulting and networking) so that they could access those resources at a later time. In sum, the project provided tools and events to help individual businesses increase operational sustainability and, in the aggregate, this contributes to sustainability of Western meat supply chains.
- Calculating costs of production
- Accurate pricing of meat products
- Working effectively with a meat processor
- Regulations around meat sales
- Labor recruitment and training
- How to differentiate a product in the marketplace
Pricing meat products for different markets
Understanding state and federal regulations around meat sales
Building a skilled workforce for processing plants
The value of recordkeeping to understand an operation's costs of production